---
title: "Running a subnational level multi-country model"
output: 
  rmarkdown::html_vignette:
       toc: true
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Running a subnational level multi-country model}
  %\usepackage[UTF-8]{inputenc}
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


## Load your library

```{r, include=TRUE, message=FALSE, eval=FALSE}
library(mcmsupply)
set.seed(1209)
```


## Get your data

```{r, include=TRUE, message=FALSE, eval=FALSE}
cleaned_subnatdata <- get_data(national=FALSE)
```


## Get the JAGS model inputs from the cleaned data

```{r, include=TRUE, message=FALSE, eval=FALSE}
pkg_data <- get_modelinputs(startyear=1990, endyear=2025.5, nsegments=12, 
                            raw_data = cleaned_subnatdata)
```


## Run JAGS model and get posterior point estimates with uncertainty. 

For speed and illustation purposes, we will use 10 itterations, with no burn in period and taking every third sample. This leaves only 9 samples. We DO NOT recommend this setting. The recommended settings are 80000 itterations, with 10000 burn in period and taking every 35th sample. This is commented out and listed underneath the below R code. 

```{r message=FALSE, eval=FALSE}
mod <- run_jags_model(jagsdata = pkg_data, jagsparams = NULL,
                      n_iter = 10, n_burnin = 1, n_thin = 3)
                     # n_iter = 80000, n_burnin = 10000, n_thin = 35)
```


# Check the model diagnostics

We use this to evaluate the convergence of the model parameters. We should expect to see R-hat values of approximately 1.05. The plot function will give you a visual summary for each parameter monitored. 

```{r, include=TRUE, message=FALSE, eval=FALSE}
plot(mod$JAGS)

print(mod$JAGS)
```

Using the ggplot2 and tidybayes R packages, we will check the trace plots to assess the convergence of individual parameters.
We expect to see a 'caterpillar' like appearance of the chains over the iterations. 

```{r, include=TRUE, message=FALSE, eval=FALSE}
sample_draws <- tidybayes::tidy_draws(mod$JAGS$BUGSoutput$sims.matrix)

var <- sample_draws %>% dplyr::select(.chain, .iteration, .draw,`P[1,2,1,1]`) %>%
  dplyr::mutate(chain = rep(1:2, each=mod$JAGS$BUGSoutput$n.keep)) %>%
  dplyr::mutate(iteration = rep(1:mod$JAGS$BUGSoutput$n.keep, 2))

ggplot2::ggplot(data=var) +
  ggplot2::geom_line(ggplot2::aes(x=iteration, y=`P[1,2,1,1]`, color=as.factor(chain)))
```

## Plot posterior point estimates with uncertainty

```{r, include=TRUE, message=FALSE, eval=FALSE}
plots <- plot_estimates(jagsdata = pkg_data, model_output = mod)
```


## Pull out estimates that you are particularly interested in

```{r, include=TRUE, message=FALSE, eval=FALSE}
estimates_2018 <- pull_estimates(model_output = mod, country = 'Nepal', year=2018)

head(estimates_2018)
```